Method and apparatus for grid multidimensional scheduling viewer

ABSTRACT

A method and apparatus for depicting grid availability at various times and for illustrating a simulation of the way in which a grid project will be executed based on grid availability. In addition, the affects of introducing new nodes into the grid may be determined and the affects on the simulated behavior of the grid with regard to the running of a grid project may be depicted using the graphical user interface. From this information, a user may determine the optimal time to initiate processing of a grid project by the computing grid.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is generally directed to an improved dataprocessing system. In particular, the present invention is directed toan improved grid computing system in which a simulation of a gridproject is generated in accordance with network and processor gridresources availability data.

2. Description of the Related Art

In the 1990's, computer scientists began exploring the design anddevelopment of a computer infrastructure, referred to as the computationgrid, whose design was based on the electrical power grids that had beenknown to date. Grid computing was initially designed for use withlarge-scale, resource intensive scientific applications, such as theSearch for Extraterrestrial Intelligence (SETI) program's computinggrid, that require more resources than a small number of computingdevices can provide in a single administrative domain. Since then, gridcomputing has become more prevalent as it has increased in popularity asa mechanism for handling computing tasks.

A computation grid enables computer resources from geographicallydistributed computing devices to be shared and aggregated in order tosolve large-scale resource intensive problems. A computational grid mayalso be referred to as just a “grid.” To build a grid, both low leveland high level services are needed. The grid's low level servicesinclude security, information, directory, and resource managementservices. The high level services include tools for applicationdevelopment, resource management, resource scheduling, and the like.Among these services, the resource management and scheduling tends to bethe most challenging to perform optimally.

Known grid computing systems, such as Legion, Data Synapse, PlatformComputing, Grid MP from United Devices, Berkley Open Infrastructure forNetwork Computing (BOINC), PBS Pro™ Grid from Altair, the Globus®Toolkit (available from Argonne National Laboratory, Chicago, Ill.), andthe Open Grid Services Architecture (OGSA), perform resource managementand scheduling based primarily upon the processor load(s) of the variousnodes, i.e. computing devices, in the computing grid with some othernon-dynamic prerequisite factors being taken into account to determinewhich nodes may be utilized in the computing grid. Thus, if a node meetsall of the non-dynamic prerequisite factors and its current processorload-is below a predetermined threshold, grid jobs may be scheduled torun on that node. If the node's processor load is above thepredetermined threshold, the node is no longer a candidate to run gridjobs until its processor load again falls below the predeterminedthreshold.

Because known grid computing systems only take into considerationprocessor load(s) as a dynamic factor for determining scheduling ofjobs, and fail to consider network traffic that the grid jobs maycreate, sub-optimal scheduling often results. As a result, the gridjobs, which are intended to be performed in an unobtrusive manner withregard to the regular functioning of the nodes, may adversely affect theexisting loads on the nodes.

Because of this sub-optimal scheduling that results due to using onlythe processor load(s) as a basis for the scheduling, many scientific andcommercial enterprises are reluctant to make use of grid computingbecause of the possible negative impact it may cause on their existinginformation technology infrastructures. First, these enterprises areuncertain about how much grid activity may disrupt their existingworkload and second, they are hesitant to use the computing grid formission critical projects because they are unable to quantify thecapacity of their grid that is necessary to run the grid jobs associatedwith the grid project within a required time span.

These problems with existing grid computing systems are rooted in thefact that resource management and scheduling in these grid computingsystems do not take into account the necessary amount of network trafficfor performing grid jobs or the affect that this traffic may have onexisting loads of nodes in the grid. Network traffic may negativelyaffect both the performance of the existing workloads on the nodes in agrid as well as the performance of the grid jobs themselves.

SUMMARY OF THE INVENTION

The aspects of the present invention provide a mechanism for presentingresource requirements in a grid computing system. Processor capacity andnetwork capacity data for a plurality of grid nodes in a grid computingsystem are identified for different periods of time. In addition,processor resources and network resources needed for execution of a gridproject are also identified. A visualization of resource usage ispresented for the grid nodes during a selected period of time within thedifferent periods of time. A determination is also made regarding gridprocessor and network availability at various times during the specifiedtime interval. This processor and network availability information canbe utilized to generate a simulation of various phases of the gridproject execution during the specified time interval. These and otherfeatures and advantages of the present invention will be described in,or will become apparent to those of ordinary skill in the art in viewof, the following detailed description of the preferred embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofan illustrative embodiment when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 is an exemplary diagram of a grid computing environment in whichaspects of the present invention may be implemented;

FIG. 2 is an exemplary diagram illustrating the interaction of the gridmanagement system and the grid agents on the various grid nodes;

FIG. 3 is an exemplary block diagram of a grid agent in accordance withone exemplary embodiment of the present invention;

FIG. 4 illustrates an exemplary process for determining the capacity ofthe links between grid nodes so that a minimum capacity link is set asthe maximum capacity available between two grid nodes in accordance withone exemplary embodiment of the present invention;

FIG. 5 is an exemplary diagram illustrating a grid management system inaccordance with one exemplary embodiment of the present invention;

FIG. 6A is an exemplary diagram of a grid project model created inaccordance with the grid project modeling language of one exemplaryembodiment of the present invention;

FIG. 6B is an exemplary illustration of a grid project modeling languagehigh level XML code script describing a grid project.

FIG. 7 is an exemplary diagram illustrating a graphical user interfacein which resource availability curves for the computing grid may bedepicted in accordance with an exemplary embodiment of the presentinvention;

FIG. 8 is an exemplary diagram illustrating one mechanism for depictingthe execution of a grid project using the resource availability plots ofone exemplary embodiment of the present invention;

FIG. 9 is an exemplary diagram illustrating a graphical depiction of thesame grid project as shown in FIG. 8 in which the start time forperforming the grid project has been changed;

FIG. 10 is an exemplary diagram illustrating the affect of an extendedgrid on the grid project of FIGS. 8 and 9 in accordance with onexemplary embodiment of the present invention;

FIG. 11 illustrates a clustering of grid nodes in accordance with oneexemplary embodiment of the present invention;

FIG. 12 is an exemplary two dimensional plot of grid node clusters basedon processor and network resource availability or capacity;

FIG. 13 illustrates the operation of the throttling mechanism inaccordance with one exemplary embodiment of the present invention;

FIG. 14 is a flowchart outlining an exemplary operation of the presentinvention when generating a visual representation of a simulation of agrid project in accordance with one exemplary embodiment of the presentinvention;

FIG. 15 is a flowchart outlining an exemplary operation of the presentinvention when scheduling the dispatching of grid jobs in accordancewith one exemplary embodiment of the present invention; and

FIG. 16 is a flowchart outlining an exemplary operation of the presentinvention when throttling the network traffic associated with a gridproject in accordance with one exemplary embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 is an exemplary diagram of a computing grid environment in whichaspects of the present invention may be implemented. As shown in FIG. 1,the computing grid 100 includes a plurality of grid nodes 110 coupledtogether via one or more networks 120-130. The grid nodes may be anytype of computing device that is capable of either processing grid jobsand grid data provided to it or is capable of providing such grid datato other grid nodes. For example, a grid node 110 may be a servercomputing device, client computing device, workstation, personalcomputer, database system, mainframe computer system, or the like.

The computing grid 100 is managed by the grid management system 140. Thegrid management system 140 communicates with agent software present onthe grid nodes 110 to obtain information about each of the grid nodes110 and to submit grid jobs and grid data to the grid nodes 110 forprocessing. The grid management system 140 provides the necessaryfunctionality for determining which grid nodes 110 in the computing grid100 are on-line, which grid nodes 110 have available capacity to handlegrid jobs, schedules the dispatching of grid jobs to the various gridnodes 110, receives and correlates the results of the grid jobsdispatched to the grid nodes 110, and performs other grid managementfunctions in accordance with the embodiments of the present inventiondescribed hereafter.

A grid project is the highest level of a piece of work on the grid. Agrid project may also sometimes be equivalently referred to by the term“grid application.” A grid job is a portion of work required to performa grid project. That is, a grid project is broken up into a plurality ofindividual grid jobs that may be dispatched to grid nodes forprocessing. A set of grid jobs performing a grid project may execute inparallel on different nodes in the grid. A grid job may also sometimesbe referred to in the art as a transaction, work unit, task orsubmission.

Each grid node 110 in the computing grid 100 may perform their normalworkload, i.e. non-grid related processing, and may also performprocessing of grid jobs submitted to them from the grid managementsystem 140. The results of the processing of these grid jobs are thenreturned to the grid management system 140 or other designated node,which combines the results of various grid jobs in order to obtain thenecessary data for completion of the grid project. Grid agent softwarepresent on the grid nodes 110 measures performance characteristics ofthe grid nodes 110 and provides these measurements to the gridmanagement system 140 so that the grid management system 140 canproperly schedule dispatching of grid jobs to the various grid nodes110.

As mentioned above, in known computing grid systems, the availableprocessor capacity of the grid node is the dynamic characteristic of thegrid nodes used as a basis for determining the scheduling of grid jobs.That is, the grid management system obtains information from the gridagent software on the grid nodes indicating the CPU utilization of agrid node over time. From this information, the grid management systemmay determine when the grid node's CPU utilization is likely to be belowa predetermined threshold and thus, is able to schedule the dispatchingof a grid job to the grid node at that predicted time.

The problem with using only CPU availability as a measure fordetermining when to dispatch grid jobs is that large amounts of data maybe sent on slow links and the grid jobs using that data may not even beexecuted near the locations where the required data is stored. Thisresults in additional network traffic. Thus, the large amount of databeing sent over slow links and the additional network traffic requireddue to inefficient placement of the data in the grid results in a largerload on the network than would be optimal. This may adversely impact theregular operations of the grid nodes. Thus, instead of an unobtrusiveuse of the grid nodes, the grid jobs actually negatively affect theregular work performed by these grid nodes.

The aspects of the present invention avoid these negative affects ofgrid computing by taking into consideration both the processorutilization and network utilization required to perform the functions ofa grid project using the computing grid. That is, the present inventionutilizes grid agents present on the grid nodes that monitor bothprocessor utilization and availability of the grid nodes, with regard toboth grid and non-grid work, and network utilization and availability ofthe links between grid nodes, to determine availability of the gridnodes to process grid jobs. This information is then used to model gridactivity, to simulate the performance of the computing grid with regardto the processing of a grid project, to schedule dispatch of grid jobsto the grid nodes, and to throttle transmission of the grid jobs andtheir data to the various grid nodes so that adverse impacts on theregular functioning of the grid nodes is avoided.

FIG. 2 is an exemplary diagram illustrating the interaction of the gridmanagement system and the grid agents on the various grid nodes of agrid computing system 200. As shown in FIG. 2, the grid managementsystem 210 communicates with the grid agents 220 on the grid nodes 230of the computing grid 240 to obtain processor and network availabilitydata. The grid agents 220 include processor utilization monitors andnetwork utilization monitors to obtain measures of processor and networkutilization with regard to grid and non-grid job processing. Theprocessor utilization monitors determine, over a period of time, howmuch of the processor resources of the grid node are being used toprocess non-grid work, process grid jobs, and how much of the processorresources are idle. The network utilization monitors determine, over aperiod of time, how much of the network capacity associated with linksto this grid node are being used and how much capacity is available.

This processor and network utilization data is transmitted to the gridmanagement system 210 where the data is stored in a database 215 forlater use. For example, the grid management system 210 of the presentinvention uses the information stored in the database 215 in generatinga grid project description model, determining a simulation of the gridproject processing performance, scheduling grid job dispatching, andthrottling of grid job data transmissions.

The grid management system 210 receives grid projects from a gridproject submission system 250. The grid project submission system 250may communicate with the grid management system 210 to submit the gridproject, request and view a simulation of the grid project, adjustdispatch parameters for the grid project based on the simulation of thegrid project, and receive results of the grid project being processed bythe computing grid 240.

When a grid project is submitted by the grid project submission system250 to the grid management system 210, the grid project may be simulatedusing the data maintained in the database 215 such that the affect ofvarious start times, various additional grid nodes, and the like, may bedetermined. From this information, the user of the grid projectsubmission system may select appropriate grid project dispatchingcriteria for use in dispatching the grid project as a plurality of gridjobs to the grid nodes 230 of the computing grid 240.

The grid management system 210 then schedules the dispatching of thegrid jobs based on the selected dispatch criteria. This scheduling ofgrid jobs may involve selecting certain grid nodes to which the gridjobs should be submitted based on non-dynamic characteristic informationabout the grid nodes maintained by the grid management system 210. Atthe schedule time(s), the grid management system 210 transmits grid jobsand/or corresponding data to the grid agents 220 on the appropriate gridnodes 230. The grid agents 220 preferably include grid client softwarethat permits the grid agents 220 to execute the grid jobs on theirrespective grid nodes 230. The dispatching and execution of grid jobs ongrid nodes is generally known in the art. For example, the Globus® orOGSA mechanisms may be used to execute grid jobs on grid nodes usinggrid client software.

FIG. 3 is an exemplary block diagram of a grid agent in accordance withone exemplary embodiment of the present invention. As shown in FIG. 3,the grid agent 300 includes a controller 310, a processor utilizationmonitor 320, a network utilization monitor 330, a network interface 340,grid client software 350, a processor/network utilization statisticsstorage 360, and a grid project throttle control 370. The grid agent 300is implemented as software instructions executed by the processor(s) ofthe grid node on which the grid agent 300 is deployed. The grid agent300 executes as a background application that is not readily discernibleto a user of the grid node on which the grid agent 300 is running.

The controller 310 controls the overall operation of the grid agent 300and orchestrates the operation of the elements 320-370. The processorutilization monitor 320 monitors the processor utilization of the gridnode with regard to both grid and non-grid workloads. The processorutilization monitor 320 distinguishes between processor utilizationassociated with grid workloads and processor utilization associated withnon-grid workloads. This distinguishing between processor utilization ofgrid workloads and non-grid workloads is accomplished by tying themonitoring functions of the processor utilization monitor 320 with thegrid client software 350.

The grid client software 350 communicates with the grid managementsystem, accepts grid jobs to be performed, executes them under anappropriate environment, and then returns results from the grid jobs.The grid client software 350 for different embodiments of the presentinvention is augmented to keep track of processes and threads doing workon behalf of the grid. That is, the grid client software 350, asprocesses are executed and threads are created, maintains a datastructure that stores an identifier of the processes or threads that areassociated with grid jobs being processed by the grid node. Statisticsregarding all threads executed by the processor(s) of the grid node aremaintained in another data structure. These statistics may include, forexample, amount of processing cycles needed to execute the thread,memory usage, disk space usage, amount of CPU time used to execute thethread, or the like.

By comparing the two data structures, the statistics associated withgrid-related threads and non-grid related threads may be differentiated.The non-grid related threads constitute workload on the systems that isnot initiated by grid work and thus, represent the normal workload onthe system. Thus, a determination may be made as to how much non-gridworkload is present on the grid nodes' processor(s) at specific times.As a result, a more accurate measurement of the actual processorcapacity of the grid node is determined.

The network utilization monitor 330 is used to generate networkutilization statistics for the grid node. Often grid jobs need a largeamount of input data which must be transmitted to the grid nodeperforming the work, i.e. processing the grid job. Large amounts ofoutput data may also be produced by grid jobs. In addition, unless gridjobs are scheduled on nodes near databases storing the required datathat the grid job may need to access, significant delays can occur dueto network traffic and the need to retrieve the required data from aremote database.

All of these situations can take up valuable network bandwidth and cancause severe congestion in the network. As a result, both the gridnode's normal workload and the grid jobs themselves may be negativelyimpacted by the consumption of network bandwidth on links to and fromthe grid node.

The grid project throttle control 370 throttles processor and networkutilization if such utilization exceeds predetermined expected processorand network availability values. This throttling mechanism, discussedmore fully below, prevents network congestion and processing delayscaused by execution of grid jobs in order to ensure that regularoperation of the grid nodes, i.e. the grid nodes usual workloads, arenot adversely affected by transmission and processing of grid jobs andgrid job data.

To collect the information needed to determine the network utilizationof the grid node, the network utilization monitor 330 uses conventionalnetwork traffic monitors to determine the amount of traffic between eachpair of grid nodes on a physical network link. That is, each grid nodeof the network includes a conventional network traffic monitor thatdetermines the amount of network traffic over links between that gridnode and the grid nodes to which that grid node is linked. The networkutilization monitor 330 for the illustrative example of the presentinvention determines the most probable route that a data packet willtravel between that grid node and other grid and non-grid nodes. Thenetwork utilization monitor then determines the minimum capacity link inthe route. For example, the network utilization monitor 330 may use thetrace route utility available in the Windows™ and Unix™ operatingsystems (i.e., the tracert or traceroute command line program) todetermine the likely route that a data packet will traverse between anytwo grid nodes. The conventional network traffic monitors of both thegrid nodes and non-grid nodes along the route may be used to determinethe capacity of the links between grid nodes so that a minimum capacitylink is set as the maximum capacity available between the two gridnodes. The maximum capacity is the maximum amount of network trafficthat a particular link can support between the two nodes.

This process is illustrated in FIG. 4. As shown in FIG. 4, a grid node410 needs to determine the maximum capacity available between itself andother grid nodes 420-450 of the computing grid 400. Each of grid nodes410-450 is equipped with a grid agent that includes a conventionalnetwork traffic monitor for monitoring the amount of traffic andavailable bandwidth along connections or links to that grid node. Inaddition, non-grid nodes 460-490 may also be present in the network thatare not part of the computing grid 400. These non-grid nodes may also beequipped with conventional network traffic monitors for monitoring theamount of traffic and available bandwidth along connections to a givengrid node.

In determining the maximum capacity available between itself and each ofother grid nodes 420-450, grid node 410, in one exemplary embodiment,runs the trace route utility using the destination IP address, UniversalResource Locator (URL), or the like, of the grid nodes 420-450 as thebasis upon which to perform the trace route operation. The IP address,URL, or the like, of the grid nodes 410-450 may be maintained in a datastructure associated with the grid agent for use in determining networkcapacity and utilization. As a result of running the trace route utilityon grid node 420's IP address, the trace route utility determines thatthe most probable route for a data packet being sent from the node 410to the node 420 is the route comprising links A and B. Similarly, theroute between node 410 and 430 is determined by the trace route utilityto be link C. The route between node 410 and node 440 is comprised ofthe links C, D and E and the route between node 410 and 450 is comprisedof the links C, F, G and H.

The network capacity along links between nodes 410-490 in the networkmay be determined using the conventional network traffic monitors of thegrid nodes 410-450 and non-grid nodes 460-490. The depicted example is asimplified version of a computing grid 400 so that many of the routesbetween grid node 410 and the other grid nodes comprise a single link(shown in dashed lines). However, in more complex computing gridconfigurations, it should be appreciated that a single route may involvelinks between three or more nodes, including both grid nodes andnon-grid nodes.

One example of a route involving multiple nodes is the route betweengrid node 410 and grid node 440. In this example, the route passesthrough grid node 430. Thus, two measurable links exist: link C betweengrid node 410 and 430, and link I between grid nodes 430 and 440. Thenetwork capacity, or available bandwidth, of each link is determined andthe minimum network capacity, or available bandwidth, is selected as themaximum network capacity between grid node 410 and grid node 440 for theparticular measurement period. Thus, values can be deduced(approximately) for network capacity available between any two gridnodes.

Returning to FIG. 3, the processor/network utilization statisticsstorage 360 is used to store both the processor utilization statisticsand network capacity or utilization statistics data collected over an“n” minute interval, where the value of “n” depends on the particularimplementation of the embodiments of the present invention and may beset to any time period. The intervals may be sent by the grid agent tothe grid management system where they may be aggregated with otherstatistical measurements obtained from that grid node and other gridnodes. The aggregated processor and network utilization information maythen be statistically evaluated to determine typical values, such asdaily, weekly, monthly, or other repetitive period values of expectedprocessor and network capacity in the computing grid.

Specifically, in one exemplary embodiment, for each collection point,grid node, and time window, a sum of values of the processor and networkavailable capacity, a sum of the squares of these values, and the numberof samples taken is stored. This storing of data permits the computationof standard deviation, mean and other statistics over the data collectedwithout needing to keep the entire set of collected data. This data maythen be used to simulate the execution of a grid project on thecomputing grid at various times of the week, month, year, etc., todetermine scheduling of the dispatching of grid jobs to grid nodes,throttling of data transmission associated with grid jobs, and the like,as discussed hereafter.

FIG. 5 is an exemplary diagram illustrating a grid management system inaccordance with the aspects of the present invention. The gridmanagement system 500 may be implemented in hardware, software, or anycombination of hardware and software. In an illustrative embodiment ofthe present invention, the grid management system 500 is implemented assoftware instructions executed by one or more data processing devices.

As shown in FIG. 5, grid management system 500 includes controller 510,network interface 520, grid project storage interface 530, grid projectstorage system 535, grid project visualization and simulation engine540, node processor/network availability storage system interface 550,node processor/network availability data storage system 555, gridproject scheduling engine 560, and grid project job dispatcher 580.These elements are in communication with one another via control/datasignal bus 590. Although a bus architecture is shown in FIG. 5, thedifferent aspects of the present invention are not limited to such andany architecture that facilitates the communication of control/datasignals between the elements described above may be used withoutdeparting from the spirit and scope of the present invention.

Controller 510 controls the overall operation of grid management system500 and orchestrates the operation of the other elements in this system.Network interface 520 provides a communication pathway for receiving andsending data. In particular, network interface 520 provides a mechanismthrough which statistical data regarding the processor and networkavailability of the various grid nodes may be received. Similarly, gridproject jobs and data may be transmitted to grid nodes via the networkinterface 520. Results of these grid project jobs may also be receivedthrough network interface 520.

Grid project storage interface 530 provides a communication interfacefor storing and retrieving grid project data from grid project storagesystem 535. The grid project data in grid project storage system 535 mayinclude the actual grid project data for generating grid project jobsand the data upon which the grid project jobs operate. Alternatively,the grid projects and the data upon which the grid project jobs operatemay be stored in one or more remotely located databases.

Grid project visualization and simulation engine 540 is used to generatesimulations of grid projects as well as a graphical user interfacethrough which the user may view the simulations to determine an optimumscheduling of the grid project jobs. Grid project visualization andsimulation engine 540 uses the processor and network availabilityinformation obtained from the various grid nodes via network interface520 and stored in node processor/network availability data storagesystem 555 via node processor/network availability data storage systeminterface 550 to determine grid processor and network availability atvarious times, such as various times of the day, week, month, or year.This information is used to determine how various start times of gridprojects will affect the completion time of the grid project as well asthe affect on the grid processor and network availability utilization,as discussed hereafter.

Grid project scheduling engine 560 is used to schedule the dispatchingof grid project jobs to grid nodes. This scheduling of the grid projectmay be performed, for example, in response to a user selecting aparticular start time for the grid project using the graphical userinterface provided by grid project visualization and simulation engine540. Grid project scheduling engine 560 breaks up the grid project andcorresponding grid project data into grid jobs and determines a time atwhich each grid job should be transmitted to its corresponding gridnode.

Grid project job dispatcher 580 actually performs the operations ofdispatching grid jobs and their corresponding data to the grid nodes viaone or more networks. Grid project job dispatcher 580 works in a similarmanner to that known in existing computing grid systems with the primarydifference being in that the grid jobs and data being transmitted by thegrid project job dispatcher 580 are determined in accordance with theoperations of grid project scheduling engine 560, and grid projectvisualization and simulation engine 540.

When a user wishes to determine the best possible scheduling for a gridproject, the user logs onto the grid management system 500 and requeststhat a particular grid project's execution over a particular time periodbe simulated. For example, when the user logs onto the grid managementsystem 500 and selects a “grid project simulation” option presentedthrough a graphical user interface (GUI) provided by the grid managementsystem 500, the user is then presented with another GUI through whichthe user may select or enter a grid project identifier, a time windowfor the simulation, for example, a start date/time and end date/time,and a statistical basis for the simulation, such as, average processoror network availability per day, average per week, average per month,one standard deviation processor/network availability per day, onestandard deviation per week, etc.

The grid project visualization and simulation engine 540 then retrievesnode processor/network availability data from the node processor/networkavailability data storage system 555 for a period of time correspondingto the particular time window selected. For example, if the user selectsa time window from Feb. 1, 2004 to Feb. 29, 2004, node processor/networkavailability data for the period of time corresponding to this timewindow is retrieved from the storage system 555. For example, the datarepresenting node processor/network availability for each day of theweek may be retrieved and repeated for each week between the Feb. 1,2004 to Feb. 29, 2004 time period to thereby simulate theprocessor/network availability for the computing grid during thedesignated time period.

This node processor/network availability data that is retrieved from thenode processor/network availability data storage system 555 representsthe total availability of the processors and network link resources inthe computing grid during the selected time period. This information maybe used to generate curves in a plot representing the availableresources of the computing grid over the selected period of time. Thesecurves are used along with information about the manner by which theselected grid project is to be dispatched to determine a simulation ofthe performance of the grid project. The information about the manner bywhich the selected grid project is to be dispatched is obtained from amodel of the grid project obtained from the grid project storage system535.

Grid project description model provides a description of the variousphases of a grid project. The phases of a grid project include, forexample, dispatching, execution, and then return of the results data.The various phases of a grid project are described by the descriptionmodel as a set of descriptions, that include, for example, the number ofgrid jobs to be dispatched, the amount of data corresponding to eachgrid job, the amount of run-time for executing the grid jobs on thedata, and the estimated amount of data corresponding to the results ofthe execution of the grid jobs. The descriptions of the various phasesalso include descriptions of activities to be performed in parallel andserially.

The grid project description model is generated using a grid projectmodeling language designed to describe the various phases of the gridproject. The description model may be generated by a user or provider ofa grid project, in accordance with the project modeling language.

The grid project modeling language uses an XML format to define asequence of phases for a grid project. FIG. 6B is an illustrativeexample of the grid project modeling language high level XML code scriptdescribing a grid project.

The grid project modeling language identifies the various phases of agrid project. The modeling language sets forth a sequence of gridproject phases. Within each phase, there may be any number ofdescriptions of parallel activities to be performed, including CPUprocessing, network activity, or nested sub-phases. Each of thedescriptions specifies the properties of the described activities, suchas processing requirements, expected network traffic, prerequisites,dependencies, activity splitting limits and characteristics, reliabilityaction, and others.

FIG. 6A depicts a diagram of a grid project model created in accordancewith the grid project modeling language of one exemplary embodiment ofthe present invention. The grid project model 600 at block 610illustrates the transfer of grid jobs and/or grid project data to one ormore grid nodes via one or more transfer files during the dispatchingphase. Blocks 630-645 depict grid nodes processing grid jobs and/or griddata in parallel during the execution phase. Although this illustrativeexample depicts a single split of grid job 620 into four grid jobs630-645 processing in parallel, there may be any number of parallelactivities performed by any number of grid nodes during this phase. Inaddition, splits into parallel activities may occur multiple timesduring the execution phase. At block 650, additional processing of theoutput from grid jobs 630-645 may occur at grid job 650. Upon completionof processing, the output from grid jobs 620-650 are transferred inparallel at blocks 660-670 back to the grid management system.

The grid project model is parseable by the illustrative examples of thepresent invention to determine the various phases of a grid project andthe various characteristics of each phase. A determination of the amountof processor and network resource requirements for execution of thevarious phases of the grid project may be made by processing the gridproject description model. The necessary processor and network resourcesare then used with the processor and network availability informationfor the selected time period to determine how long it will take for eachphase of the grid project to be performed.

For example, the required usage of processor and network capacity inorder to perform the phases of the grid project are represented as areasbounded by the processor and network availability curves generated basedon the grid resource availability information obtained from the gridnodes. Because these areas are bounded by the resource availabilitycurves, if there is less availability of a resource than is necessary toperform a particular portion of a phase of the grid project, more timewill be required to achieve the required area. This may be done for eachphase of the grid project so that a complete illustration of theexecution of a grid project in relation to the resource availability ofthe computing grid is viewable to a user.

FIG. 7 is an exemplary diagram illustrating a graphical user interfacein which resource availability curves for the computing grid may bedepicted in accordance with an exemplary embodiment of the presentinvention. As shown in FIG. 7, the graphical user interface (GUI) 700includes fields 710 and 715 for entering a start time/date and endtime/date for the simulation. Field 720 is provided for selecting astatistical basis for the simulation representation and field 730 isused to designate a granularity for the plot of the simulation of thegrid project. Field 740 is used to select a graphical presentation type,such as, time line chart or other type of graphical representation.Field 750 provides a field through which a particular grid project maybe selected for simulation and portion 760 provides a plot of theresource availability curves determined from the node processor/networkavailability data retrieved according to the selected time window, i.e.start time and end time.

As shown in FIG. 7, the portion 760 includes two line graphs—one linegraph element 770 illustrating processor availability over a recurringperiod of time within the time window, and other line graph element 780illustrating network availability over the recurring period of time. Itshould be appreciated that the granularity of the time periodillustrated in the portion 760 may be of various levels. For example,rather than a reoccurring period of time within the time window, theentire time window may be illustrated in portion 760 or any subportionthereof. In addition, the period of time illustrated may be changed orscrolled by the user.

The line graphs 770 and 780 represent the upper boundary of processorand network resources that may be utilized by the grid project. Astatistical plot, such as that shown in line graphs 770 and 780, mayrepresent at least one of an actual processor and network availability,averages of the processor and network resource availability, onestandard deviation from the actual or average processor and networkresource availability, or any combination thereof. Furthermore, otherelements may be represented in the statistical plot, either alone or incombination with the actual processor and network availability, averagesof the processor and network resource availability, one standarddeviation from the actual or average processor and network resourceavailability.

These line graphs 770 and 780 provide the basis upon which to determinehow much time is required to complete each phase of the grid project.

FIG. 8 is an exemplary diagram illustrating one mechanism for depictingthe execution of a grid project using the resource availability plots ofone exemplary embodiment of the present invention. As shown in FIG. 8,in one exemplary embodiment of the present invention, each phase of thegrid project is depicted as areas under the grid lines representing thecomputing grid resource availability. The phases of the grid projectinvolve phases in which processor resources are dominant and phases ofthe grid project in which network resources are dominant.

These phases are depicted in FIG. 8 in different shadings of the areasbelow the processor and network resource availability. Lightly shadedareas 810, 820 and 830 represent phases of the grid project in whichnetwork availability is a dominant factor in determining the performanceof the grid project. Darker shaded areas 840 and 850 represent portionsof the grid project where processor availability is a dominant factor.It should be appreciated, however, that rather than differentiatingbetween phases based on dominant resources, the aspects of the presentinvention may operate on phases of the grid project in which bothresources are considered equally important to the determination of gridproject performance.

As shown in FIG. 8, the grid project is comprised of a phase ofexecution, represented by area 810, in which the grid project transfersgrid jobs and grid project data to the grid nodes. A second phase ofoperation, represented by area 840, is a representation of the gridnodes processing the grid jobs and data transmitted to them. A thirdphase of operation, represented by area 820, is a representation of atransmission of some additional data for processing by the grid nodes. Afourth phase of the grid project, represented by the area 850, is arepresentation of the grid nodes performing some additional processing.The fifth phase of the grid project, represented by the area 830,represents the grid nodes transmitting data back to the grid managementsystem, such as, results of the execution of the grid project.

The phases of the grid project 810, 820 and 830 that are dominated bynetwork resource availability are primarily bounded by the line graphrepresenting the network resource availability. As shown in FIG. 8,there are portions of the areas 810, 820 and 830 that exceed theprocessor availability line graph yet are below the network availabilityline graph. Similarly, there is a portion of the area 840 where the area840 exceeds the network availability line graph yet is below theprocessor availability line graph.

The depiction of the areas under the resource availability line graph ismade based on a selected start point, the phases of the grid projectdefined by the grid project model, and the resource availability dataobtained from the grid nodes and maintained in the nodeprocessor/network availability data storage system. The user may modifythe start time by moving a cursor over the depiction 800 or otherwiseentering a different start time. The grid management system modifies thedepiction 800 of the grid project performance based on the change in thestart time. In this way, the user may see the affect of different starttimes on the performance of the grid project.

For example, if the user selects an earlier start time, this results ina different amount of processor and network resources being available toperform the various phases of the grid project. If this different amountof processor and network resources results in a lower amount ofresources being available for a particular phase, then the time periodfor completing this phase of the grid project may be increased.Similarly, if the different amount of processor and network resourcesresults in a higher amount of resources being available, the time periodfor performing this phase of the grid project may be shortened.

As shown in FIG. 8, phases dominated by processor resources and phasesdominated by network resources are depicted simultaneously in thegraphical depiction of the simulation of the grid project performance.Thus, both the affects of available network resources and availableprocessor resources on the performance of the grid project areillustrated in the graphical depiction of the grid project'sperformance. As the start time of the grid project changes, the size ofareas 810-850 are kept consistent even though the dimensions, i.e. timeversus amount of resource, may be modified based on the amount ofavailable processor and network resources.

FIG. 9 is an exemplary diagram illustrating a graphical depiction of thesame grid project as shown in FIG. 8 in which the start time forperforming the grid project has been changed. As shown in FIG. 9, inresponse to the start time being changed, an end time of the gridproject has also changed. This is because, while the amount of processorand network resources needed to complete the grid project has notchanged, the availability of the processor and network resources haschanged due to the change in the start time of the grid project. Thus,by altering the start time for a grid project to begin, the end time forthe completion of the grid project change as a result. This may beimportant with regard to deadlines for completing grid projects.

Thus, the aspects of the present invention provide a graphical userinterface through which a depiction of the grid project's performance900 with regard to both the available processor and network resources isprovided. The graphical user interface permits the user to modify thestart times of the grid project with the depiction 900 of the gridproject being modified dynamically as the start times are changed. Inthis way, a user may determine whether the grid project will becompleted within a necessary time period taking into account theprocessor and network resource usage by the grid nodes executing thegrid project.

While the above illustrative examples have described in terms of theprocessor and network resource availability data obtained from gridnodes, in a further example of the present invention, the affect ofadding additional grid nodes on the performance of a grid project may bedetermined using the graphical user interface. Similar to changing thetime at which the grid project is started, the introduction ofadditional grid nodes into the computing grid changes the amount ofprocessor and network resources. The aspects of the present inventionmay modify the upper bounds of the available processor and networkresources based on the additional resources provided by the addition ofgrid nodes from other sources than are typically available in thecomputing grid. The affect of these additional resources on theperformance of the grid project may then be depicted in a similar manneras that described above.

For example, a user may determine that the processor and networkresource availability from the established computing grid is notsufficient to perform the grid project in a manner to achieve thepurposes and deadlines of the user. The provider of the computing gridmay have entered into an agreement with other computing system providersto provide conditional computing system and/or network resources whennecessary. Many such agreements may be present for different groups ofprocessor and/or network resources. The inclusion of each of thesegroups and the resulting affect on the performance of a grid project maybe determined using the graphical user interface.

The user of the graphical user interface may be provided with amechanism in the graphical user interface to select viewing of asimulation of the grid project with an extended grid. In addition, theextent of the extension of the grid may be selectable. As a result,resource availability data for these additional grid nodes, which isobtained in a similar manner as described above with regard to the basicgrid nodes, may be retrieved from the node processor/networkavailability data storage system and used to modify the processor andnetwork resource availability line plots that define the upper bound onthe phase areas of the grid project. Since additional resources are madeavailable by incorporating additional grid nodes, the result ofincluding these additional grid nodes tends to shorten the amount oftime necessary to complete the performance of the grid project.

This result is illustrated in FIG. 10 in which an extended grid's affecton the grid project of FIGS. 8 and 9 is depicted. As shown in FIG. 10,the inclusion of additional processor and network resources causes theline plots to represent larger amounts of resources being available. Asa result, the time period from the start time to the completion of thegrid project is made shorter. As with the previous embodiments of thepresent invention, the user may again modify the start time of the gridproject and see the affect on the performance of the grid project withinthe selected extended computing grid on the depiction 1000 of the gridproject's performance.

Once the simulation of the grid project has been presented and the userhas determined an appropriate start time for the grid project, as wellas whether an extended grid should be used, the grid management systemschedules the dispatching of the grid project jobs to particular gridnodes. Part of this process is generating grid project jobs based on thegrid project model and selecting grid nodes to which the grid projectjobs and data are to be transmitted. The generation of grid project jobsbased on a grid project is generally known in the art and thus, adetailed description is not provided herein.

In selecting grid nodes to which grid jobs are to be dispatched, thenetwork nature of the grid project is first determined. For example, thegrid project scheduling engine determines if the grid project is one of:

a hub and spoke type of grid project in which quantities of data aresent to and from the grid jobs being executed on the grid agents of thegrid nodes from a submission point with minimal other communicationsduring job processing;

a grid project in which grid jobs primarily access data from a databaselocated at a specific location on the computing grid; and

a grid project in which grid jobs communicate extensively with eachother during their processing.

Based on the determination of the nature of the grid project, aclustering algorithm is then used to map sets of grid nodes based on howwell they are connected to the submission point, a database at thedesignated location, or based on the grid nodes' mutualinterconnectivity. Clustering algorithms are generally known in the artand thus, a detailed description of the clustering algorithms is notprovided herein. The basis for the clustering algorithm is determinedbased on the type of grid project determined above. The resultingclusters are then ranked in accordance with processor capacity andnetwork capacity, having already subtracted processor and networkcapacity consumed by other non-grid or grid work.

For example, a first table of grid node clusters is generated ranked byprocessor capacity. A second table of grid node clusters is generatedranked by network capacity. These two tables are then used to generate atwo-dimensional plot of the clusters based on both processor and networkcapacity. Initial minimum requirements of processor and networkresources for selection of clusters of nodes to execute the grid jobsare then established. A determination is then made as to whether theclusters of grid nodes that are above both minimum requirements havesufficient capacity to perform the work required of the grid project. Ifnot, the initial minimum processor and network capacity is adjusted sothat more clusters are evaluated.

This process may be repeated until the clusters above the minimumrequirements provide sufficient capacity to execute the grid project oruntil successive recalculations show diminishing or even reduced returnswhen using more clusters. If a set of clusters is identified that wouldmeet the capacity requirements for executing the grid project within thetimeline indicated by the simulation of the grid project, or anotherwise set timeline, then the set of clusters is used to schedule theperformance of the grid project. If there is evidence of diminishing oreven reduced returns when recalculating the minimum processor andnetwork capacities and evaluating the additional clusters, then anindication that a suitable scheduling cannot be accomplished may bereturned to the user.

FIG. 11 illustrates a clustering of grid nodes in accordance with oneexemplary embodiment of the present invention. As shown in depiction1100, grid nodes are clustered in accordance with their networkproximity to each other, to a source of grid jobs, or to databases fromwhich data is accessed for performing the grid jobs. Network clusteringalgorithms are generally known in the art and the embodiments of thepresent invention may make use of any known network clustering algorithmfor determining clusters of grid nodes.

Based on the particular clustering algorithm used, various clusters,such as clusters 1150-1170, may be generated. An average processor andnetwork capacity for each cluster over the time period for performingthe grid project, as selected using the graphical user interface andsimulation mechanism of the embodiments of the present invention, forexample, is determined for each cluster. That is, for example, the totalprocessor and network capacity for each grid node in the cluster issummed and the sums of the time period of interest are averaged in orderto determine the average resource availability of the cluster.

The averages of the processor and network resource availability orcapacity are then used to rank the clusters in corresponding tables 1180and 1190. Table 1180 is a table of the identified clusters ranked byaverage processor capacity. Table 1190 is a table of the identifiedclusters ranked by average network capacity. These tables are used toplot the clusters in a two dimensional graph of processor capacityversus network capacity.

FIG. 12 is an exemplary two dimensional plot of grid node clusters basedon processor and network resource availability or capacity. As shown inplot 1200, in this exemplary embodiment, the x-axis is network capacityand the y-axis is processor capacity. The clusters are then plotted onthis graph based on their determined average processor and networkcapacities.

A minimum threshold for processor and network capacities is thendetermined. These minimums are selected by the user or by aspecification in the scheduled grid project. The minimums arerepresented as lines 1210 and 1220. A determination is then made as towhether the clusters that are plotted in the upper right of the graph,such as above the minimum lines 1210 and 1220, have sufficient totalprocessor and network capacity to accomplish the execution of the gridproject within the time period established by the simulation of the gridproject, or the time period otherwise specified by the user. If so, thenthe clusters identified in the upper right of the graph are selected asthe clusters to which grid jobs are to be dispatched and the schedulingof these grid jobs being processed by the selected clusters isperformed.

If, however, the total capacity of the clusters in the upper right ofthe graph is not sufficient to complete the grid project within the timeperiod requested, then the minimum processor and network resourcecapacity lines 1210 and 1220 are recalculated and a larger set ofclusters is evaluated. As a result, new minimum capacity lines 1230 and1240 are generated and the clusters above and to the right of theselines are evaluated to determine if their total resource capacities aresuch that the grid project will be completed within the selected timeperiod. This process may be repeated until a sufficiently large size ofclusters with sufficient resource capacity is identified or untiladditional recalculations result in diminished or reduced returns.

Once a set of clusters are identified, the grid project schedulingengine generates grid jobs for each of the grid nodes in the clustersand schedules the dispatching of these grid jobs to the grid nodes suchthat the execution of the grid project within the selected time periodmay be accomplished. The actual dispatching of the grid jobs to the gridnodes is performed in a known manner using a grid project job dispatcherwhich transmits the grid job, and optionally the data upon which thegrid job is to operate, to the grid nodes at a scheduled time.

As mentioned above, recalculation of the minimum processor and networkresource capacity lines possibly may result in diminishing or reducedreturns. In such cases, an option may be provided to extend thecomputing grid in a similar manner as discussed above with regard to thesimulation of the grid project. That is, additional grid nodes may beintroduced into the basic computing grid in accordance with establishedrelationships with potential grid node providers.

When a determination is made that a suitable scheduling of the gridproject cannot be accomplished, the user may be provided with an optionto consider the impact of extending the computing grid on the ability toschedule the grid project. If the user elects to extend the computinggrid, the user may be prompted to indicate which additional grid nodesare to be added to the computing grid. That is, if a plurality ofpossible sources of additional grid nodes are present, then the user mayselect the source or sources from which these additional grid nodes areobtained.

Once the user elects to extend the computing grid and selects the sourceor sources from which the additional grid nodes are obtained, theclustering and plotting may be performed again. As a result, additionalclusters of grid nodes are generated with additional processor andnetwork resources. The same processes as discussed above with regard todetermining a set of clusters that permit the grid project to beexecuted within the selected time period is performed. If the discoveryof the set of clusters results in a set of clusters being determinedthat permit the grid project to be completed within the selected timeperiod, then these grid nodes are used to schedule the dispatching ofgrid jobs. This scheduling may involve coordinating with the additionalgrid node source computing system to request access to the additionalgrid nodes for performing processing on the grid jobs. Thus, a dynamicextension of the computing grid is made possible in order to schedulethe dispatching of grid jobs so that a grid project may be completedwithin the time period determined through simulation of the gridproject.

Thus, the aspects of the present invention provide a mechanism by whicha user may obtain a simulation of the performance of a grid projectbased on measured processor and network resource availability/capacityof grid nodes over a selected period of time. The user may see theaffect of changing the start time of a grid project on the way in whichthe grid project will be executed in the computing grid and, moreimportantly, the affect on the completion time/date of the grid project.This permits the user to determine the optimal time to initiate the gridproject on the computing grid so that performance goals are achieved.

In addition, the aspects of the present invention provide a mechanismfor scheduling the dispatching of grid jobs on the computing grid,whether using the simulation mechanism as a basis for this scheduling ornot. The scheduling mechanism permits the selection of an optimal set ofclusters of grid nodes to which grid jobs are to be dispatched in orderto complete the grid project within a time period selected by a user.The scheduling mechanism may iteratively expand the set of clustersconsidered when it is determined that a current set of clusters will nothave sufficient processor and/or network availability or capacity tocomplete the grid project within the designated time deadline.

Both with the simulation mechanism and with the scheduling mechanism,the embodiments of the present invention provide a mechanism forexpanding the basic computing grid by including additional grid nodesthrough arrangements made with potential grid node suppliers or sources.The additional grid nodes may be mapped into the computing grid and theaffect of the additional grid nodes on the simulated behavior of thegrid project and/or the clustering and resource availability of theclusters of grid nodes may be determined. In this manner, adetermination may be made as to whether expanding the computing gridwill result in the grid project being completed within a desired timeperiod and if so, expansion of the computing grid may be requested fromthe additional grid node supplier/source.

In addition to the above, the aspects of the present invention furtherprovide a grid project throttle control for controlling the execution ofgrid jobs on a grid node. The throttling mechanism operates so that apredetermined limit on the amount of network traffic associated withgrid jobs is maintained. That is, with the simulation mechanism asdescribed above, a maximum data transfer amount is determined based onthe available capacity of the network as determined from the nodeprocessor/network availability data. This maximum data transfer, ornetwork traffic, for the grid jobs may then be reported by the gridproject throttle control to the grid agents associated with grid nodesso that they may regulate the amount of grid data processed by theirnetwork interfaces. Thus, the grid management system dispatches gridjobs and data to the grid nodes in accordance with this throttlingmechanism. The grid nodes also transfer data between grid nodes, as wellas the grid management system in accordance with the throttlingmechanism. In this way, the grid project is kept from overwhelming thenetwork and is slowed down to a rate that has been calculated to bereasonably unobtrusive to the normal operation of the grid nodes.

The throttle control operates by identifying a set of parametersidentifying network activity for a grid job on a grid node. The set ofparameters includes one or more parameters identifying network activity.For example, the set of parameters could set for a rate of expectednetwork availability and/or a rate of expected processor availabilityfor a grid job. If the grid node has an application that supportscontrolling data transmission based on the set of parameters, the set ofparameters can be sent to the application on the grid node. The rate ofdata transmission from the grid node may then be controlled based on therate of data transmission from the grid node and the set of parameters.The rate of data transmission can thereby be limited by the throttlingmechanism if the rate of data transmission exceeds the expected networkavailability.

FIG. 13 illustrates the operation of the grid project throttle controlin accordance with one exemplary embodiment of the present invention. Asshown at 1300, the grid project scheduling engine 1310 provides resourcerequirements information, including expected network and processoravailability information, to the grid project throttle control 1320,indicating the amount of network resources, such as, for example, anamount of bandwidth that will be required by each grid job during eachphase of the grid project's execution. This information is then packagedinto a wrapper 1325 associated with the grid job 1330 and/or grid jobdata and is provided by the scheduling engine 1310 to the grid agent1340 in association with the grid job and/or grid job data. The networkinterface's Application Program Interfaces (APIs) 1350 of the networkinterface 1360 strips off the wrapper 1325 and processes the expectedavailability information which is then used to control the rate at whichgrid data is sent from the grid node 1380 across the network.

That is, the network interface 1360 processes the information indicatingthe expected network availability. The network resources that grid node1380 may utilize during its phases of operation, indicated by theexpected network availability threshold, in which grid data istransmitted by the grid node 1380 to other computing devices. Theexpected network availability information places a limit on the rate atwhich data from the grid agent may be transmitted by the networkinterface 1360. As a result, the network interface 1360 may limit therate at which grid data is retrieved from buffers 1370 associated withports 1375 corresponding to the grid agent 1340, and transmitted overthe network.

In this manner, the transmission of data by a grid node is controlled bya network interface 1360 of the grid node in conjunction with throttlinginformation received from the grid project throttle control 1320.

The grid project throttle control 1320 operates to initiate throttlingof network utilization for grid jobs when a determination is made thatthe rate of data transmission attributable to grid work exceeds theexpected network availability threshold for data transmission at thegrid node, such that data transmission does not exceed the expectednetwork availability. In this way, the grid throttle control 1320controls the rate at which grid traffic is sent from the grid node 1380so that the grid traffic does not negatively affect the normal operationof the grid node 1380.

The grid project throttle control 1320 also operates to throttle controlof processing of grid jobs by the grid node. The grid node processingresources that the grid node may utilize to process grid jobs areindicated by an expected processor availability threshold. Adetermination is made as to whether the rate of processor utilizationattributable to grid work at the grid node exceeds an expected processorutilization availability threshold for processor utilization. Thethrottle control 1320 limits the rate of processor utilization by a gridnode if the rate of processor utilization exceeds the expected processorutilization threshold for the grid job, so that the grid node processingof non-grid jobs is not negatively affected by grid jobs executing atthe grid node.

The expected network and processor availability values are determinedbased on the scheduling of the grid jobs as determined by the grid jobscheduler, the grid project visualization and simulation engine, oroptionally a combination of the grid job scheduler and the grid projectvisualization and simulation engine. The grid project throttle control1320 also operates to make a determination as to network and processorutilization at a grid node attributable to grid work and non-grid work.The throttle control 1320 may make this determination based upon thetask identification, or other means known in the art. The throttlecontrol also collects statistics on system usage, such as processor andnetwork usage for grid and non-grid work. Statistics regarding processorutilization and network utilization are collected from processorutilization monitors and network utilization monitors located on thegrid nodes. The throttle control 1320 can subtract out the gridcontribution to the grid node work load.

The throttling mechanism provides a further benefit in that thisthrottling mechanism provides a mechanism through which network activitymonitors associated with the grid agents may categorize network trafficinto grid and non-grid categories, as previously discussed above. Thatis, since the network interfaces throttle the network traffic emanatingfrom the grid node, the network activity monitors know that the networktraffic associated with grid jobs cannot be greater than the establishedexpected network availability threshold. Thus, if the network link isoperating at full capacity, then the maximum that may be attributable togrid jobs is the throttling threshold. In these embodiments, fullcapacity is, for example, all of the bandwidth consumed.

FIGS. 14-16 are flowcharts outlining the various operations of some ofthe embodiments of the present invention previously described above. Itwill be understood that each step of the flowchart illustrations, andcombinations of steps in the flowchart illustrations, can be implementedby computer program instructions. These computer program instructionsmay be provided to a processor or other programmable data processingapparatus to produce a machine, such that the instructions which executeon the processor or other programmable data processing apparatus createmeans for implementing the functions specified in the flowchart step orsteps These computer program instructions may also be stored in acomputer-readable memory or storage medium that can direct a processoror other programmable data processing apparatus to function in aparticular manner, such that the instructions stored in thecomputer-readable memory or storage medium produce an article ofmanufacture including instruction means which implement the functionsspecified in the flowchart step or steps.

Accordingly, steps of the flowchart illustrations support combinationsof means for performing the specified functions, combinations of stepsfor performing the specified functions and program instruction means forperforming the specified functions. It will also be understood that eachstep of the flowchart illustrations, and combinations of steps in theflowchart illustrations, can be implemented by special purposehardware-based computer systems which perform the specified functions orsteps, or by combinations of special purpose hardware and computerinstructions.

FIG. 14 is a flowchart outlining an exemplary operation for generating avisual representation of a simulation of a grid project in accordancewith one exemplary embodiment of the present invention. As shown in theflowchart 1400 of FIG. 14, a request is received from a user for asimulation of execution of a grid project over a particular period oftime at step 1410. A request may be made for the user to enter or selectthe parameters for the requested grid project simulation at step 1420 byproviding the user with a GUI requesting a grid project identifier, astart date, a start time, an end date, an end time, a statistical basisfor the simulation, the type of graphical presentation, and/or any otheradditional parameters.

The simulation of the grid project is generated based upon theparameters entered by the user and the network availability andprocessor availability data. At step 1430, network and processoravailability data is retrieved from the network and processoravailability data storage system. A resource availability plot isgenerated at step 1440 based upon the processor/network availabilitydata. The resource availability plot at step 1440 and, optionally,information from a grid project model description, as described above,is used to generate a simulation of execution of grid project, at step1450.

FIG. 15 is a flowchart outlining an exemplary operation for schedulingthe dispatching of grid jobs in accordance with one exemplary embodimentof the present invention. As shown in the flowchart 1500 of FIG. 15,grid management system receives a grid project submission at step 1510.The network nature of the grid project is determined at step 1515. Atstep 1520, grid node clusters are generated utilizing a networkclustering algorithm. Any known network clustering algorithm fordetermining clusters of grid nodes may be utilized to generate grid nodeclusters.

A determination of average resource availability is made at step 1525,regarding network and processor availability for each cluster. At step1530, each cluster of grid nodes is ranked according to the averages ofthe processor and network resource availability.

A minimum threshold value for processor capacity and a minimum thresholdvalue for network capacity are determined at step 1535. The minimumthreshold values may be selected by the user or by a specification inthe grid project. At step 1540 a determination is made as to whether agrid node cluster exceed both processor and network minimum thresholdvalues and has sufficient total network and processor capacity toexecute the grid job. Grid nodes that exceed both minimum thresholds andhave sufficient capacity may be selected. Step 1545 schedules the gridproject in accordance with the selected grid node clusters.

If no clusters satisfy the requirements of step 1540, then the minimumthreshold values are adjusted downward at step 1555 to permit evaluationof additional clusters. The process may be repeated until a cluster thatsatisfies the requirements of step 1540 is identified and scheduling canoccur at step 1545 or until additional recalculations result indiminished or reduced returns at step 1550, in which case an indicationis provided to the user that scheduling cannot be accomplished at step1560.

FIG. 16 is a flowchart outlining an exemplary operation for throttlingthe network traffic associated with a grid project in accordance withone exemplary embodiment of the present invention. As shown in theflowchart 1600 of FIG. 16, the expected network availability andexpected processor availability threshold is determined at step 1610. Ifthe rate of data transmission exceeds the expected network availabilitythreshold at step 1620, the throttle control throttles the rate of datatransmission at step 1630. If the rate of processor utilization exceedsthe expected processor availability threshold at step 1640, then thethrottle control throttles the rate of processor utilization forprocessing grid jobs back down to the expected processor availability atstep 1650.

The throttle control also collects statistics on system usage ofprocessor and network resources. The throttle control is capable ofsubtracting out the grid job's contribution to the processor and networkload. The throttle control can identify grid work by task identificationor other means known in the art. The throttle control will only throttleprocessor and network activity associated with grid work. In thismanner, the grid project throttle control program associated with thegrid agent on a grid node controls the rate of network/processorutilization at the grid node to prevent grid jobs from interfering withthe execution of non-grid work.

It is important to note that while the present invention has beendescribed in the context of a fully functioning data processing system,those of ordinary skill in the art will appreciate that the processes ofthe present invention are capable of being distributed in the form of acomputer usable medium of instructions and a variety of forms and thatthe present invention applies equally regardless of the particular typeof signal bearing media actually used to carry out the distribution.

The invention can take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In a preferred embodiment, the invention isimplemented in software, which includes but is not limited to firmware,resident software, microcode, etc.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any apparatus thatcan contain, store, communicate, propagate, or transport the program foruse by or in connection with the instruction execution system,apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk—read only memory (CD-ROM), compactdisk—read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A computer implemented method for presenting resource requirements ina grid computing system, the computer implemented method comprising:identifying processor capacity and network capacity data for a pluralityof grid nodes in the grid computing system for different periods oftime; identifying processor resources and network resources needed by agrid project; and presenting a visualization of resource usage for theplurality of grid nodes for a selected period of time within thedifferent periods of time.
 2. The computer implemented method of claim 1further comprising: determining grid processor and network availabilityduring the different periods of time; and generating a simulation ofvarious phases of grid project execution in accordance with theprocessor and network capacity during the different periods of time. 3.The computer implemented method of claim 1, wherein the identifying stepcomprises: collecting data on actual resource usage in the plurality ofgrid nodes to form collected data; and identifying available processorcapacity and network capacity in the plurality of nodes using thecollected data.
 4. The computer implemented method of claim 1, furthercomprising: generating a statistical plot of processor and networkresource usage for the selected period of time within the differentperiods of time.
 5. The computer implemented method of claim 4, whereinthe statistical plot represents at least one of an actual processorcapacity and network availability, an average of processor capacity andnetwork availability, one standard deviation from the actual processorcapacity and network availability, and one standard deviation from theaverage network capacity and processor availability.
 6. The computerimplemented method of claim 1, wherein a user may alter a start time ofthe grid project execution, and wherein the simulation is modifieddynamically in accordance with the altered start time.
 7. The computerimplemented method of claim 1, wherein a user may introduce additionalgrid nodes into the grid computing system, and wherein the simulation ismodified dynamically in accordance with the additional grid nodes. 8.The computer implemented-method of claim 1, further comprising:requesting grid project parameters, wherein a user may select or enterparameters for execution of the grid project through a graphical userinterface.
 9. The computer implemented method of claim 8, wherein thegrid project parameters include a start time, a start date, a designatedgranularity for a plot, a type of graphical presentation, a grid projectidentifier, a time window for the simulation, and a statistical basisfor the simulation.
 10. The computer implemented method of claim 3,wherein collected data is retrieved from a grid node processor/networkcapacity storage system for the selected period of time within thedifferent time periods.
 11. The computer implemented method of claim 1,wherein a grid project modeling language description is utilized todetermine the processor resources and network resources needed by thegrid project.
 12. A computer program product comprising: a computerusable medium having computer usable program code for presentingresource requirements in a grid computing system, the computerimplemented method comprising: computer usable program code foridentifying processor capacity and network capacity data for a pluralityof grid nodes in the grid computing system for different periods oftime; computer usable program code for identifying processor resourcesand network resources needed by a grid project; and computer usableprogram code for presenting a visualization of resource usage for theplurality of grid nodes for a selected period of time within thedifferent periods of time.
 13. The computer program product of claim 12,further comprising: computer usable program code for determining gridprocessor and network availability at various times during a specifiedtime interval; and computer usable program code for generating asimulation of various phases of the grid project execution in accordancewith processor and network capacity during the specified time interval.14. The computer program product of claim 12, wherein the computerusable program code for identifying processor capacity and networkcapacity data for the plurality of grid nodes in the grid computingsystem for different periods of time comprises: computer usable programcode for identifying available processor capacity and network capacityin the plurality of nodes using data on actual resource usage in theplurality of grid nodes.
 15. The computer program product of claim 12,further comprising: computer usable program code for generating astatistical plot of processor and network resource usage for a selectedperiod of time within the different periods of time.
 16. The computerprogram product of claim 15, wherein the statistical plot may representan actual processor capacity and network availability, an average ofprocessor capacity and network availability, one standard deviation fromthe actual processor capacity and network availability, or one standarddeviation from the average network capacity and processor availability.17. The computer program product of claim 12, wherein a user may alter astart time of the grid project execution, and wherein a simulation ofvarious phases of grid application execution is modified dynamically inaccordance with the altered start time.
 18. The computer program productof claim 12, wherein a user may introduce additional grid nodes into thegrid computing system, and wherein a simulation of various phases ofgrid application execution is modified dynamically in accordance withthe additional grid nodes.
 19. The computer program product of claim 12,further comprising: computer usable program code for requesting gridproject parameters, wherein a user-may select or enter parameters forexecution of the grid project through a graphical user interface.
 20. Anapparatus for simulating execution of a grid project, comprising: Acomputer comprising: a bus; a storage device connected to the bus,wherein the storage devices contains a computer usable program code; aprocessor unit, wherein the processor unit executes the computer usableprogram code to identify processor capacity and network capacity datafor a plurality of grid nodes in the grid computing system for differentperiods of time; identify processor resources and network resourcesneeded by a grid project; and present a visualization of resource usagefor the plurality of grid nodes for a selected period of time within thedifferent periods of time.